Multi-task learning (MTL) [1]} is a type of learning algorithm that aims to combine several pieces of information from different tasks to improve the model performance and its ability to better generalize [2]}. The basic idea of MTL is that different tasks can share a representation of common characteristics [2]}, and thus train them jointly. The use of different data sets from different tasks allows learning an efficient representation of the common characteristics of all tasks, because all data sets are used to obtain it, even if each task has a small data set, thus improving the performance of each task.
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